This model is a fine-tuned version of bert-large-uncased on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset. It achieves the following results on the evaluation set:

Loss: 5.7778
Accuracy: 0.1539

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 208
eval_batch_size: 208
seed: 10
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100000
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
6.8769	0.36	10000	6.7582	0.1101
6.4647	0.71	20000	6.4764	0.1314
6.3679	1.07	30000	6.3218	0.1407
6.252	1.42	40000	6.2139	0.1454
6.2132	1.78	50000	6.1398	0.1478
6.0407	2.13	60000	6.0774	0.1502
6.0694	2.49	70000	6.0303	0.1516
5.9996	2.84	80000	5.9893	0.1521
5.9166	3.2	90000	5.9553	0.1526
5.8915	3.55	100000	5.9261	0.1530
5.8924	3.91	110000	5.8996	0.1534
5.8972	4.26	120000	5.8814	0.1533
5.8454	4.62	130000	5.8626	0.1532
5.8104	4.97	140000	5.8494	0.1534
5.8461	5.33	150000	5.8378	0.1534
5.8476	5.68	160000	5.8246	0.1536
5.7255	6.04	170000	5.8155	0.1532
5.8431	6.39	180000	5.8068	0.1537
5.7526	6.75	190000	5.7981	0.1537
5.7826	7.1	200000	5.7886	0.1537

Framework versions

Transformers 4.37.1
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.1

gokuls/BERT_pretraining_h_100_wo_deepspeed

BERT_pretraining_h_100_wo_deepspeed

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gokuls/
BERT_pretraining_h_100_wo_deepspeed